First, we evaluated the performance of the DCRNN by studying its capabilities to reproduce empirically observed neural activity patterns, and compared it to a VAR model, like that typically used for the analysis of brain connectivity with Granger causality21,46. We showed that the DCRNN can also ...
First, we evaluated the performance of the DCRNN by studying its capabilities to reproduce empirically observed neural activity patterns, and compared it to a VAR model, like that typically used for the analysis of brain connectivity with Granger causality21,46. We showed that the DCRNN can also ...
a graph attention network based on causal inference named causal graph attention network(C-GAT) is proposed to improve the robustness of the network.The model first calculates the causal weights between the neighborhood of the target node and its label and uses them to sample the neighborhood....
Causal Inference and Discovery in Python May 2023 456 pages 4.5 (49) eBook $21.99 $31.99 ADD TO CART Interpretable Machine Learning with Python Oct 2023 606 pages 4.9 (21) eBook $27.98 $39.99 ADD TO CART Hands-On Graph Neural Networks Using Python Apr 2023 354 pages 4.1 (23...
1) Recommendation Loss: To make predictions for the target question qtqt, we first combine its representation qtqt with the student presentation sNsN, and then map them through a neural network to a low-dimensional embedding, so as to calculate the probability that the student answers the targe...
【6】 RegExplainer: Generating Explanations for Graph Neural Networks in Regression Task 亚利桑那州立大学 http://arxiv.org/pdf/2307.07840v1 Graph regression is a fundamental task and has received increasing attention in a wide range of graph learning tasks. However, the inference process is often...
GNN: graph neural network Contributed by Jie Zhou, Ganqu Cui, Zhengyan Zhang and Yushi Bai. Content 1. Survey 2. Models 2.1 Basic Models 2.2 Graph Types 2.3 Pooling Methods 2.4 Analysis 2.5 Efficiency 2.6 Explainability 3. Applications 3.1 Physics 3.2 Chemistry an...
Hence, how to estimate the superimposed causal effect and recover the individual treatment effect in the presence of interference becomes a challenging task in causal inference. In this work, we study causal effect estimation under general network interference using GNNs, which are powerful tools for...
2024 Graph Local Homophily Network for Anomaly Detection CIKM 2024 Link Link 2024 Effective Illicit Account Detection on Large Cryptocurrency MultiGraphs CIKM 2024 Link Link 2024 LEX-GNN: Label-Exploring Graph Neural Network for Accurate Fraud Detection CIKM 2024 Link Link 2024 Collaborative Fraud Detecti...
popularity debiased; self-supervised learning; recommendation; graph neural network1. Introduction In recent years, recommendation systems (RS) have emerged in order to mitigate the effect of information overload. Due to the advantages in improving platform effectiveness and user satisfaction, ...